
@menu
* General Overview::
* Citing MetaQTL::
* How to Get and Install MetaQTL::
* Getting Help::
* General Usage of the Programs::
* References::
@end menu

@node General Overview
@section General Overview

@menu
* What is MetaQTL::
* Definition of the Problem::
@end menu

@node What is MetaQTL
@subsection What is MetaQTL

@b{MetaQTL} is a suite of programs designed to carry out meta-analysis of QTL mapping experiments. A QTL mapping experiment consists in a genetic linkage map and a set of quantitative trait loci (QTL) which have been detected and positioned onto the genetic linkage map. This package is composed by several programs written in pure Java. These programs can perform various tasks, including reformatting, analyzing data and visualizing the results of the analyses. Presently the programs can handle data from backcross, intercrosses and recombinant inbreds, as well as a few other experimental designs. This project is ongoing and suggestions are welcome for further improvements and enhancements.

@node Definition of the Problem
@subsection Definition of the Problem

Since the last decade, the advent of molecular markers have accelerated the pace of discovering the loci which are implied in quantitative trait variation. Quantitative Trait Loci (QTL) mapping usually begins with the collection of genotypic (based on molecular markers) and phenotypic data from a segregating population. First, from the genoptypic data the markers are both ordered and positioned on a genetic map using standard linkage mapping approaches. Secondly, refinement of analytical methods have enabled to detect one ore several QTL on each chromosome (see for instance Lander and Botstein 1989, Zeng 1994). Nevertheless due to the limiting number of individuals and generations in usual experiment this approach generally leads to QTL locations with a confidence interval (CI) around 10 cM (Kearsey 1998) which in plant generally corresponds to a thousand of genes or more.

Due to its relative simplicity and its compelling concept QTL mapping has been widely used and more and more QTL detection results are now available in public databases (e.g in maize at @url{http://www.maizegdb.org}). One of the main purpose of these databases was to facilitate the comparison of different QTL detection results by providing both standard description of these results and ontologies (see for instance the trait ontology at @url{http://www.gramene.org/plant\_ontology/}). Relevance of comparative analysis of QTL studies have been illustrated by several authors (Khavkin 1997 and 1998, Lin 1995). However these studies often relied on simple descriptive statistics.

QTL congruency study was partially improved thanks to Goffinet and Gerber (2000) who proposed a meta-analysis based approach in order to integrate QTL results from several experiments. Their method makes it possible to evaluate how many ``actual'' QTL locations underly the distribution of the observed QTL on the genome. This approach has been implemented in BioMercator by Arcade et al. (2004). This software allows user to merge both markers and QTL onto a consensus map by means of an iterative projection procedure. Then the algorithm devised by Goffinet and Gerber (2000) can be applied to evaluate the likelihood of clustering the observed QTL in 1,2,3 or 4 groups. Afterward, the optimal number of clusters is selected by using a Akaike like criterion. Alghough original this approach suffers from the absence of indicator to assess the consensus map quality and from the limiting number of QTL clusters which can be explored.

Based on recent methodological developments, MetaQTL implements a series of Java programs in order to carry out whole-genome QTL meta-analysis. All the programs in MetaQTL are command line programs. Each program does a small job and the user can easily combine the programs as a group to do a complete analysis. 

@node Citing MetaQTL
@section Citing MetaQTL

In publications you should cite this manual.

@itemize @bullet
@item Veyrieras J-B., Goffinet B. and Charcosset A., 2005. MetaQTL, Version @value{VERSION}. INRA, France.
@end itemize

@node How to Get and Install MetaQTL
@section How to Get and Install MetaQTL

@b{MetaQTL} is avalaible at @url{http://bioinformatics.org/mqtl}. As @b{MetaQTL} is entirely written in Java (1.5) it should be run on most of operating systems provided that a compatible Java Virtual Machine (1.5) has been previously installed on your machine. Otherwise go to @uref{http://java.sun.com/downloads/index.html}: then download and install the last version of the Java 2 Platform.
To run the programs of @b{MetaQTL} you must have updated the Java @var{CLASSPATH}  by adding the path to the jar archive @file{metaqtl.jar} included into the @b{MetaQTL} distribution. Under an UNIX like operating system this can be done as follows
@cartouche
@smallexample
@verbatim
bash$ export CLASSPATH=${CLASSPATH}:/path/to/metaqtl/directory/metaqtl.jar
@end verbatim
@end smallexample
@end cartouche
Under Windows 9x/NT/2000/XP you can look at @url{http://support.microsoft.com/} to find how to update or create the environment variable @var{CLASSPATH}. Generally, to view or change environment variables you have to apply the following steps:
@itemize @bullet
@item Right-click My Computer, and then click Properties.
@item Click the Advanced tab.
@item Click Environment variables.
@item Click one the following options, for either a user or a system variable:
@itemize @bullet
@item Click New to add a new variable name and value.
@item Click an existing variable, and then click Edit to change its name or value (here the value will be the entire file path from the root of the disk to the jar archive of MetaQTL).
@end itemize
@end itemize
@noindent
In the next sections, the examples are given assuming the @var{CLASSPATH} have been correctly set.

@node Getting Help
@section Getting Help

For any questions or troubles about the use of @b{MetaQTL}, contact J.B. Veyrieras by email at @email{veyrieras@@moulon.inra.fr}.

@node General Usage of the Programs
@section General Usage of the Programs

The programs in the @b{MetaQTL} suite all have the same look and feel and are heavily influenced by UNIX programs. They can only be used as command line programs. The options @code{-v, --verbose} and @code{-h, --help} are common to all the programs. The first one activates the verbosity mode and the second one can be used to display a brief help on program usage.

@node References
@section References

@itemize @bullet
@item A. Arcade, A. Labourdette, M. Falque, B. Mangin, F. Chardon @i{et al.}, 2004, @i{BioMercator: integrating genetic maps and QTL towards discovery of candidate genes}, Bioinformatics, 20:2324-2326.
@item A.P. Dempster, N.M. Laird, D.B. Rubin, 1977, @i{Maximum Likelihood from incomplete data via the EM algorithm (with  discussion)}, J. Roy Stat. Soc., B 39:1-38.
@item B. Goffinet and S. Gerber., 2000, @i{Quantitative trait loci: a meta-analysis}, Genetics, 155(1):463-473.
@item M.J. Kearsey and A.G.L. Farquhar., 1998, @i{QTL analysis in plants; where are we now?}, Heredity, 80:137-142.
@item E. Khavkin and E. H. Coe., 1997, @i{Mapped genomic locations for developmental functions and QTLs reflect concerned groups in maize (Zea mays L.)}, Theor. Appl. Genet., 95:343-352.
@item E. Khavkin and E. H. Coe., 1998, @i{The major quantitative trait loci for plant stature, development and yield are general manifestations of developmental gene clusters}, Maize Newslett., 72:60-66.
@item E. S.Lander and D. Botstein, 1989, @i{Mapping Mendelien factors underlying quantitative traits using RFLP linkage maps}, Genetics, 121:185-199.
@item Y. R. Lin, K. F. Schertz, A. H. Paterson, 1995, @i{Comparative analysis of QTLS affecting plant height and maturity across the poaceae, in reference to an interspecific sorghum population}, Genetics, 141:391-411.
@item T. Sasaki, T. Matsumoto, K. Yamamoto, K. Sakata, 2002, @i{et al.}, @i{The genome sequence and structure of rice chromosome 1}, Nature, 420(6913):312-316.
@item J-B. Veyrieras, B. Goffinet, A. Charcosset, 2005, @i{Meta-analysis of QTL Mapping Experiments}, in prep., 2005.
@item J. H. Ward., 1963, @i{Hierarchical Grouping to Optimize an Objective Function}, J. Am. Stat. Assoc., 58:236-244.
@item Z-B. Zeng, 1994, @i{Precision mapping of quantitative trait loci}, Genetics, 136(4):1457-1468.
@end itemize
